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swMATH22407MaRDI QIDQ34199

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Related Items (42)

Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties ⋮ pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach ⋮ An estimator for local analysis of genome based on the minimal absent word ⋮ NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC ⋮ Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition ⋮ Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC ⋮ IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition ⋮ Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach ⋮ Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions ⋮ Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia ⋮ Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm ⋮ BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC ⋮ Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC ⋮ Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence ⋮ Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC ⋮ iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences ⋮ Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC ⋮ Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition ⋮ Effective DNA binding protein prediction by using key features via Chou's general PseAAC ⋮ iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou's general PseAAC ⋮ Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components ⋮ Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions ⋮ Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach ⋮ iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components ⋮ SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins ⋮ Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components ⋮ Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC ⋮ Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins ⋮ iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features ⋮ Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC ⋮ Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection ⋮ Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space ⋮ Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts ⋮ Using weighted features to predict recombination hotspots in \textit{Saccharomyces cerevisiae} ⋮ mLASSO-Hum: a LASSO-based interpretable human-protein subcellular localization predictor ⋮ Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition ⋮ iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou's general PseAAC ⋮ Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier ⋮ Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction

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